This invention relates, in its broadest aspect, to preventing the escalation of process and equipment problems into serious incidents. It achieves this by first providing the operator with an early warning of a developing process or equipment problem, before the alarm system is activated, and by then providing the operator with key information for localizing and diagnosing the root cause of the problem.
In the petrochemical industry, abnormal operations can have a significant economic impact (lost production, equipment damage), can cause environmental releases, and, in more severe cases, can endanger human life. An industrial consortium has estimated that abnormal events can cost between 3 and 8% of production capacity, which is over $10 billion for the US petrochemical industry.
Abnormal situations commonly result from the failure of field devices (such as instrumentation, control valves, and pumps) or some form of process disturbance that causes the plant operations to deviate from the normal operating state. In particular, the undetected failure of key instrumentation and other devices, which are part of the process control system can cause the control system to drive the process into an undesirable and dangerous state. Early detection of these failures enables the operations team to intervene before the control system escalates the failure into a more severe incident.
The current commercial practice is to notify the console operator of a process problem through process alarms. These process alarms are defined by setting safe operating ranges on key process measurements (temperatures, pressures, flows, levels, and compositions). An alarm is given to the operator when the safe operating range of a measurement is violated. In current commercial practice, setting these alarm ranges is a delicate balance between giving the operator sufficient time to respond to the process problem and overwhelming him with a flood of alarms. Often, the safe operating ranges of key process measurements are set wide to reduce low importance alarms. The negative result of these wide safe-operating ranges is that abnormal conditions can advance too far, and the operator is not left with enough time to take corrective actions to mitigate the abnormal event.
In the past decade, the application of multivariate statistical models principal components models, PCA, and partial least squares, PLS) for monitoring complex industrial processes has started to take place in a few industries, notably steel casting (U.S. Pat. No. 6,564,119, pulp and paper (U.S. Pat. No. 6,522,978 and semiconductor manufacturing (U.S. Pat. No. 5,859,964. The general practice is to first identify a particular process problem, and then to build a PCA model specifically designed to catch the process problem. This model is executed online to generate statistical indices of the operation. A notification is given to the process operator based on the violation of key statistical limits (sum of square prediction error and Hotelling T square) computed from the model. The operators are then informed of a prioritized list of those original inputs, which are the greatest contributors to the statistical indices.
For each industry, the characteristics of the process operation and the related process data require modifications to the method for developing multivariate statistical models and their subsequent use in an online system. Without these modifications, there can be a number of technical problems limiting the usefulness in applying models for online process monitoring. These technical problems can cause the statistical indices have significant Type I and Type II errors (false positives and missed abnormal events).